Purpose:

The ability to predict the development of rheumatoid arthritis (RA) in patients with an early-onset undifferentiated arthritis (UA) is highly required if the remission or an adequate response to the treatment are the main goal. The statement of purpose was to develop a predictive algorithm combining clinical variables, serological biomarkers and power Doppler ultrasonography (PDUS) for the progression from an early-onset UA to RA in daily rheumatological practice.

Method:

A predictive algorithm was developed after a 12 months study of 149 adult patients with a recent-onset UA and a clinical involvement of the hands. The combination of five routine assessment variables (sex, symptoms duration, morning stiffness, anti-citrulline antibodies and IgM-rheumatoid factor) and PDUS findings was investigated. Logistic regression analysis was performed to identify the independent factors for the development of RA and global predictive score was calculated. The area under the ROC curve (AUC) was used to evaluate the diagnostic performance of the algorithm and a cut off point, maximizing both sensitivity (SE) and specificity (SP), was selected. The post-test probability (post-TP) was evaluated using the Fagan's nomogram.

Results:

During the follow up period, 62 patients (41.6%) developed a RA. The score of the global predictive algorithm ranged from 0 to 10, being a higher score indicative of a higher risk to develop RA. The algorithm demonstrated excellent discriminative ability, with an AUC of 0.951 [SE 0.019; 95% interval of confidence (CI) from 0.903 to 0.980]. With the optimal cut-off point of 5 SE was 93.6% (95% CI from 80.1% to 96.3%), SP was 89.9% (95% CI from 81.3% to 95.1%) and positive likelihood ratio (LR+) was 9.39. If a threshold of 7 was applied a higher value of SP (98.1%) was obtained, but SE (58.1%) decreased (LR+ of 31.02). The post-TP value of the two different cut-off points mentioned above were 66% and 87%, respectively.

Conclusion:

Our predictive algorithm, which include PDUS assessment, revealed an excellent discriminative ability for assessing the likelihood of development of RA. Further studies are required to confirm the results and to tailor a therapeutic approach in patients with an early-onset UA, according to the values of the predictive algorithm proposed in the present study.